• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.97-111
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• 2009

Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.

• Information Systems Review
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• v.17 no.3
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• pp.159-181
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• 2015

This study proposes a comprehensive model of purchasing intention of customers in agricultural products online shopping malls. In this study, we derived the factors through the literature reviews and logical reasoning and classified the factors as a business point of view, an information systems point of view and an agricultural characteristics point of view, and developed the integrated research model which is the factors affect purchase intentions by mediating trust and the perceived usefulness. A total of 329 samples of a valid survey data from the members of small agricultural online shopping malls were collected and the research model was empirically analyzed by a confirmatory factor analysis and path analyses using structural equation modeling with the data. The results show that the product quality and the service quality of the business point of view have effects on the trust, however the price adequacy and entertainment have no effect on the trust and the perceived usefulness respectively, also the advertising exposure has no effect on the trust but it has an effect on the purchase intention directly. The information quality and the ease of use of the information systems point of view have an effect on the trust and perceived usefulness. At last, the seasonal product of the agricultural characteristics point of view has effects on perceived usefulness but the regional brand has no effect on the trust. The results of this study provide strategic implications for successful development and operation of agricultural products online shopping malls.

• Information Systems Review
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• v.22 no.3
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• pp.119-142
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• 2020

This study aims to examine the role of IT features as heuristic cues in choosing a content on YouTube. According to the heuristic-systematic model, people tend to rely on heuristic cues when they have to choose and process useful information quickly so that they could save time and reduce demands for thinking. Based on this line of reasoning, this study posits that YouTube users rely on certain IT features as heuristic cues in choosing contents before they actually watch them. Based on the prior literature and interviews with YouTube users, we develop a research model in which social endorsement, self-presentation, and interactivity are identified as potential determinants of users' attitude toward contents, which in turn influence their intention to watch them. To empirically test the research model, we conduct a laboratory experiment and a follow-up survey. The results of data analysis show that social endorsement for the content, YouTube creator's self-presentation, and interactivity have significant and positive effects on their attitude toward the content, leading to their intention to watch it. This study suggests that IT features on YouTube could be wisely utilized to increase the chance that users choose a particular content out of many competing contents when they search certain information on YouTube.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.151-175
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• 2015

Although three types of the information security measures (technical, physical and managerial ones) are all together critical to maintaining information security in the organizations and should be implemented at the same time, this study aims at providing theoretical basis of establishing and implementing effective managerial security measures. The rationale behind this research objective is that it is very important to effectively perform the managerial security measures to achieve the target performance level of the technical and the physical security measures because main agents of practicing the information security measures in the organizations are staff members even though the technical and the physical ones are well constructed and implemented. In particular, this study intends to develop and propose the theoretical model applicable to providing the way of improving organizational members' intention to use information security technologies since the very intention to use them is essential to effectively establishing and promoting managerial security measures. In order to achieve the objective of this study, the factors critical to influencing upon the intention to use information security technologies are derived through systematically reviewing related theories and previous studies, and then the research model and hypotheses are proposed by logically reasoning the casual relationship among the these factors. Also, the empirical analyses are performed by conducting the survey of the organization members of domestic large companies and analyzing the structural equation model by PLS (Partial Least Squares) method. The significant results of this study can contribute to expanding the research area of managerial information security and can be applied to suggesting the practical guidelines for effectively establishing and implementing the managerial security measures in various organizations.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.732-748
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• 2009

The technology of Semantic Web realizes the base technology for context-awareness that creates new services by dynamically and flexibly combining various resources (people, concepts, etc). According to the realization of ubiquitous computing technology, many researchers are currently working for the embodiment of web service. However, most studies of them bring about the only predefined results those are limited to the initial description by service designer. In this paper, we propose a new service supporting model to provide an automatic method for plan related tasks which achieve goal state from initial state. The inputs on an planner are intial and goal descriptions which are mapped to the current situation and to the user request respectively. The idea of the method is to infer context from world model by DL-based ontology reasoning using OWL domain ontology. The context guide services to be loaded into planner. Then, the planner searches and plans at least one service to satisfy the goal state from initial state. This is STRIPS-style backward planner, and combine OWL-S services based on AI planning theory that enabling reduced search scope of huge web-service space. Also, when feasible service do not find using pattern matching, we give user alternative services through DL-based semantic searching. The experimental result demonstrates a new possibility for realizing dynamic service modeler, compared to OWLS-XPlan, which has been known as an effective application for service composition.

• Atmosphere
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• v.25 no.2
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• pp.295-308
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• 2015

Based on the CMIP5 historical simulation datasets, we assessed the performance of state-of-the-art climate models in respect to the relationship between interannual variabilities of the North Pacific synoptic eddy (NPSE) and East Asian winter monsoon (EAWM). Observation (ERA-Interim) shows a high negative correlation (-0.73) between the interannual variabilities of East Asian winter monsoon (EAWM) intensity and North Pacific synoptic eddy (NPSE) activity during the period of 1979~2005. Namely, a stronger (weaker) EAWM is related to a weaker (stronger) synoptic eddy activities over the North Pacific. This strong reverse relationship can be well explained by latitudinal distributions of the surface temperature anomalies over East Asian continent, which leads the variation of local baroclinicity and significantly weakens the baroclinic wave activities over the northern latitudes of $40^{\circ}N$. This feature is supported by the distribution of the meridional heat flux (${\overline{{\nu}^{\prime}{\theta}^{\prime}}}$) anomalies, which have negative (positive) values along the latitudes $40{\sim}50^{\circ}N$ for strong(weak) EAWM years. In this study, the historical simulations by 11 CMIP5 climate models (BCC-CSM1.1, CanESM2, GFDL-ESM2G, GFDL-ESM2M, HadGEM2-AO, HadGEM2-CC, IPSL-CM5A-LR, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, and NorESM1-M) are analyzed for DJF of 1979~2005. Correlation coefficient between the two phenomena is -0.59, which is comparable to that of observation. Model-to-model variation in this relationship is relatively large as the range of correlation coefficient is between -0.76 (HadGEM2-CC and HadGEM2-AO) and -0.33 (MRI-CGCM3). But, these reverse relationships are shown in all models without any exception. We found that the multi-model ensemble is qualitatively similar to the observation in reasoning (that is, latitudinal distribution of surface temperature anomalies, variation of local baroclinicity and meridional heat flux by synoptic eddies) of the reverse relationship. However, the uncertainty for weak EAWM is much larger than strong EAWM. In conclusion, we suggest that CMIP5 models as an ensemble have a good performance in the simulation of EAWM, NPSE, and their relationship.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.3
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• pp.29-50
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• 2020

The purpose of this study is to analyze the characteristics of the media contained in textbooks for students with disabilities based on the information processing model, and to find ways to utilize library materials for class improvement for them. To this end, the media included in the in-depth learning activities of the Korean language textbooks of the 2015 revised special education basic curriculum were analyzed. As a result of the analysis, it was found that students with disabilities received information mainly through vision, process information through understanding, and use language intelligence to produce results. Specifically, they accepts learning contents through illustrations and texts, processes the contents based on understanding such as reasoning and explanation, and then uses linguistic intelligence such as writing and speaking to produce results. Based on the results of this analysis, a practical method to utilize library materials in the Korean language class of students with disabilities was proposed as follows. Developing a variety of input mediums based on reading stages and collection mapping for students with disabilities. Providing book materials through reading and listening. Teaching appropriate methodological knowledge to self-directly solve advanced learning activities. In addition, developing types of writing and writing strategies that can help various production activities.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.422-427
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• 2010

Korean Game industry, especially MMORPG(Massively Multiplayer Online Game) has been rapidly expanding in these days. But As game industry is growing, lots of online game security incidents have also been increasing and getting prevailing. One of the most critical security incidents is 'Game Bots', which are programs to play MMORPG instead of human players. If player let the game bots play for them, they can get a lot of benefic game elements (experience points, items, etc.) without any effort, and it is considered unfair to other players. Plenty of game companies try to prevent bots, but it does not work well. In this paper, we propose a behavior pattern model for detecting bots. We analyzed behaviors of human players as well as bots and identified six game features to build the model to differentiate game bots from human players. Based on these features, we made a Naive Bayesian classifier to reasoning the game bot or not. To evaluated our method, we used 10 game bot data and 6 human Player data. As a result, we classify Game bot and human player with 88% accuracy.

• Therapeutic Science for Rehabilitation
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• v.12 no.3
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• pp.45-59
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• 2023

Objective : This study aims to identify the status of education in cognitive rehabilitation (CR) in occupational therapy departments of Korean universities/colleges and to analyze the educational needs for professional competencies. Methods : This study was conducted by distributing a questionnaire to professors. The questionnaire extracted items related to professional competencies from the results of a previous Delphi study. A total of 39 respondents from 32 (51.6%) of 62 universities/colleges were analyzed. The questionnaire analysis was conducted using Excel 2010 and SPSS 18.0 to analyze the Borich requirements and the priority of education through the Locus for focus model. Results : The priority of competency in CR was followed by "clinical reasoning ability to explain cognitive problems from the occupational performance perspective", "ability to manage insurance billing for CR", "ability to establish a CR plan based on outcome evaluation", "ability to perform occupation-oriented CR", and "ability to solve problems that occur during CR evaluation and intervention". In the Locus for focus model, items such as occupation-based cognitive assessment, intervention, and skills for documentation were high priorities for education. Conclusion : This study is expected to reflect educational competencies for CR and establish a plan for CR specialists through continuing education.